Artificial intelligence design charts for predicting friction capacity of driven pile in clay

In this study, five nonlinear prediction tools are used to model and predict the friction capacity of driven piles installed in clay including classical support vector machine (SVM) and two of its variants, namely regularized generalized proximal SVM and twin SVM, adaptive neuro-fuzzy inference system (ANFIS) and genetic programming (GP). The undrained shear strength, effective vertical stress, pile diameter and pile length are taken as the input parameters of the developed models, and the friction capacity is considered as the output. A total of 80 experimental observations are collected and used to train and test several models estimating the friction capacity of a driven pile in clay. The results demonstrate that, noting the root-mean-square error (RMSE) value, for prediction of the friction capacity of driven piles in clay the ANFIS model gives a better convergence to the in situ results, compared with the GP and SVM models. The developed ANFIS model provided a simple and reliable design structure for proper selection of the pile friction capacity of driven piles installed in clay. Furthermore, a simple mathematical formula is presented based on the GP model. The predicted results are compared with in situ data set models to demonstrate the abilities of the AI models. In order to perform a model evaluation, in addition to RMSE, the regression coefficient of determination is obtained through testing and training of the SVM, ANFIS and GP models. The results show high reliability for the developed models. The presented ANFIS and GP models are introduced as new models in the field of geotechnical engineering.